A first worker node of a distributed system computes a first set of gradients using a first neural network model and a first set of weights associated with the first neural network model. The first set of gradients are transmitted from the first worker node to a second worker node of the distributed system. The second worker node computes a first set of synchronized gradients based on the first set of gradients. While the first set of synchronized gradients are being computed, the first worker node computes a second set of gradients using a second neural network model and a second set of weights associated with the second neural network model. The second set of gradients are transmitted from the first worker node to the second worker node. The second worker node computes a second set of synchronized gradients based on the second set of gradients.
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3. The distributed system of claim 2, wherein the second set of gradients are computed based on the training data.
A distributed system for machine learning training involves multiple computing nodes that collaboratively optimize a model by exchanging gradient updates. The system addresses the challenge of efficiently training large-scale models across distributed hardware, where communication overhead and synchronization delays can degrade performance. The system includes a first set of computing nodes that compute gradients based on a subset of training data and a second set of computing nodes that compute gradients based on the same or different training data. The second set of gradients are specifically derived from the training data, ensuring that the model updates incorporate diverse information from the dataset. The system may also include a synchronization mechanism to align the gradients from different nodes before applying them to the model, improving convergence and accuracy. The distributed architecture allows for parallel processing, reducing training time while maintaining model performance. This approach is particularly useful for deep learning applications where large datasets and complex models require significant computational resources.
4. The distributed system of claim 1, wherein the first set of gradients includes gradients for a first layer of the first neural network model and gradients for a second layer of the first neural network model, wherein the gradients for the second layer are computed prior to the gradients for the first layer.
7. The method of claim 6, wherein at least a portion of the first set of weights are adjusted while at least a portion of the second set of synchronized gradients are computed.
9. The method of claim 5, wherein at least a portion of the first set of gradients and at least a portion of the first set of synchronized gradients are computed simultaneously.
10. The method of claim 5, wherein at least a portion of the second set of gradients and at least a portion of the second set of synchronized gradients are computed simultaneously.
12. The method of claim 5, wherein the second neural network model is different than the first neural network model.
This invention relates to neural network systems for processing data, particularly where multiple neural networks are used in sequence. The problem addressed is the inefficiency or suboptimal performance that can occur when identical neural networks are used in different stages of a processing pipeline, as this may not account for variations in data characteristics or processing requirements at each stage. The invention involves a method where a first neural network model processes input data to generate intermediate outputs. These outputs are then further processed by a second neural network model, which is structurally or functionally different from the first. The differences between the two models may include variations in architecture, parameters, training data, or optimization objectives, allowing the second model to better handle the transformed data from the first model. This approach improves performance by tailoring each neural network to its specific role in the pipeline, rather than using a one-size-fits-all model. The method may be applied in various domains, such as image processing, natural language understanding, or predictive analytics, where sequential neural network processing is beneficial. The use of distinct models enhances accuracy, efficiency, or adaptability compared to systems relying on identical models for all stages.
15. The non-transitory computer-readable medium of claim 14, wherein at least a portion of the first set of weights are adjusted while at least a portion of the second set of synchronized gradients are computed.
17. The non-transitory computer-readable medium of claim 13, wherein at least a portion of the first set of gradients and at least a portion of the first set of synchronized gradients are computed simultaneously.
This invention relates to machine learning systems, specifically optimizing gradient computation in distributed training environments. The problem addressed is the inefficiency in computing gradients across multiple nodes, which can lead to synchronization delays and reduced training speed. The solution involves a method for computing gradients in parallel while maintaining synchronization between nodes to ensure accurate model updates. The system includes a plurality of computing nodes, each configured to compute gradients for a machine learning model. A first set of gradients is computed by a first node, while a second set of gradients is computed by a second node. To improve efficiency, at least a portion of the first set of gradients and at least a portion of a first set of synchronized gradients are computed simultaneously. The synchronized gradients are derived from the first set of gradients and are used to update the model parameters across nodes. This parallel computation reduces idle time and accelerates the training process. The invention also includes a non-transitory computer-readable medium storing instructions for performing these operations. The method ensures that gradient computations are synchronized without unnecessary delays, improving the overall performance of distributed machine learning training. This approach is particularly useful in large-scale training scenarios where synchronization overhead can significantly impact efficiency.
18. The non-transitory computer-readable medium of claim 13, wherein at least a portion of the second set of gradients and at least a portion of the second set of synchronized gradients are computed simultaneously.
This invention relates to optimizing gradient computations in machine learning systems, particularly for training neural networks. The problem addressed is the computational inefficiency in gradient synchronization during distributed training, where gradients from different processors or nodes must be aggregated before updating model weights. This synchronization step often creates bottlenecks, slowing down training. The invention provides a method for computing gradients in parallel across multiple processors or nodes while maintaining synchronization. A first set of gradients is computed for a first portion of a neural network, and a second set of gradients is computed for a second portion. The second set of gradients is synchronized with a second set of synchronized gradients from other processors or nodes. The key improvement is that at least part of the second set of gradients and at least part of the second set of synchronized gradients are computed simultaneously, reducing idle time and improving efficiency. This parallel computation allows for faster convergence during training without sacrificing accuracy. The invention also includes a system with multiple processors, each computing gradients for different portions of the neural network, and a synchronization mechanism that ensures gradients are aligned before weight updates. The method can be implemented on a non-transitory computer-readable medium containing instructions for executing the parallel gradient computation and synchronization steps. This approach is particularly useful in large-scale distributed training environments where minimizing synchronization delays is critical.
20. The non-transitory computer-readable medium of claim 13, wherein the second neural network model is different than the first neural network model.
This invention relates to a system for processing data using multiple neural network models. The system addresses the challenge of improving accuracy and efficiency in machine learning tasks by leveraging distinct neural network architectures. The first neural network model processes input data to generate an initial output, while a second neural network model, which differs in architecture or configuration from the first, further refines or transforms this output. The second model may be optimized for specific tasks, such as feature extraction, classification, or regression, that the first model is not ideally suited for. By using different models, the system can enhance performance, reduce bias, or adapt to varying data distributions. The models may be trained independently or jointly, depending on the application. This approach is particularly useful in scenarios where a single model cannot achieve optimal results, such as in complex decision-making systems or multi-modal data processing. The invention is implemented via a non-transitory computer-readable medium containing instructions for executing the described neural network operations.
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March 30, 2020
October 11, 2022
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